Abstract

Development and application of metal matrix composite materials and increased application of calculations, simulations and modeling in the area of semi-solid solidification ask for the knowledge of compocasting for these materials. In this study, a self-organizing hierarchical particle swarm optimizer is implemented for computational modeling and optimization of the compocast high strength and highly uniform Al matrix composites. The matrix of the composite was a 6061 Al alloy and the reinforcement was alumina particle (Al2O3p). Experimental results were obtained for hardness, tensile and fatigue properties of the Al alloys with different vol.% of micro-particles. The tensile strength of the composites increased considerably by increasing the reduction ratio in the cold rolling process. It is observed that the presence of reinforcement in the Al alloy degrades the low-cycle fatigue property when the Al matrix composites are subject to strain-controlled cyclic loading. The method combines position update rules, the standard velocity and the strengths of particle swarm optimization with the ideas of selection, crossover and mutation from GA.

Shabani MO, Mazahery A (2012) The performance of various artificial neurons interconnections in the modelling and experimental manufacturing of the composits. Materiali in tehnologije 46(2):109–113Google Scholar